OpenRaiser/Intern-Atlas huggingface dataset:
EmotionCaps: Enhancing Audio Captioning Through Emotion-Augmented Data Generation
Venue: arXiv cs.SD 2024
Year: 2024

Recent progress in audio-language modeling, such as automated audio captioning, has benefited from training on synthetic data generated with the aid of large-language models. However, such approaches for environmental sound captioning have primarily focused on audio event tags and have not explored leveraging emotional information that may be present in recordings. In this work, we explore the benefit of generating emotion-augmented synthetic audio caption data by instructing ChatGPT with additional acoustic information in the form of estimated soundscape emotion. To do so, we introduce EmotionCaps, an audio captioning dataset comprised of approximately 120,000 audio clips with paired synthetic descriptions enriched with soundscape emotion recognition (SER) information. We hypothesize that this additional information will result in higher-quality captions that match the emotional tone of the audio recording, which will, in turn, improve the performance of captioning models trained with this data. We test this hypothesis through both objective and subjective evaluation, comparing models trained with the EmotionCaps dataset to multiple baseline models. Our findings challenge current approaches to captioning and suggest new directions for developing and assessing captioning models.
Differential Privacy on Trust Graphs
Venue: arXiv cs.CR 2024
Year: 2024

We study differential privacy (DP) in a multi-party setting where each party only trusts a (known) subset of the other parties with its data. Specifically, given a trust graph where vertices correspond to parties and neighbors are mutually trusting, we give a DP algorithm for aggregation with a much better privacy-utility trade-off than in the well-studied local model of DP (where each party trusts no other party). We further study a robust variant where each party trusts all but an unknown subset of at most $t$ of its neighbors (where $t$ is a given parameter), and give an algorithm for this setting. We complement our algorithms with lower bounds, and discuss implications of our work to other tasks in private learning and analytics.
Taking off the Rose-Tinted Glasses: A Critical Look at Adversarial ML Through the Lens of Evasion Attacks
Venue: arXiv cs.LG 2024
Year: 2024

The vulnerability of machine learning models in adversarial scenarios has garnered significant interest in the academic community over the past decade, resulting in a myriad of attacks and defenses. However, while the community appears to be overtly successful in devising new attacks across new contexts, the development of defenses has stalled. After a decade of research, we appear no closer to securing AI applications beyond additional training. Despite a lack of effective mitigations, AI development and its incorporation into existing systems charge full speed ahead with the rise of generative AI and large language models. Will our ineffectiveness in developing solutions to adversarial threats further extend to these new technologies? In this paper, we argue that overly permissive attack and overly restrictive defensive threat models have hampered defense development in the ML domain. Through the lens of adversarial evasion attacks against neural networks, we critically examine common attack assumptions, such as the ability to bypass any defense not explicitly built into the model. We argue that these flawed assumptions, seen as reasonable by the community based on paper acceptance, have encouraged the development of adversarial attacks that map poorly to real-world scenarios. In turn, new defenses evaluated against these very attacks are inadvertently required to be almost perfect and incorporated as part of the model. But do they need to? In practice, machine learning models are deployed as a small component of a larger system. We analyze adversarial machine learning from a system security perspective rather than an AI perspective and its implications for emerging AI paradigms.
Generative AI's aggregated knowledge versus web-based curated knowledge
Venue: arXiv cs.HC 2024
Year: 2024

his paper explores what kinds of questions are best served by the way generative AI (GenAI) using Large Language Models(LLMs) that aggregate and package knowledge, and when traditional curated web-sourced search results serve users better. An experiment compared product searches using ChatGPT, Google search engine, or both helped us understand more about the compelling nature of generated responses. The experiment showed GenAI can speed up some explorations and decisions. We describe how search can deepen the testing of facts, logic, and context. We show where existing and emerging knowledge paradigms can help knowledge exploration in different ways. Experimenting with searches, our probes showed the value for curated web search provides for very specific, less popularly-known knowledge. GenAI excelled at bringing together knowledge for broad, relatively well-known topics. The value of curated and aggregated knowledge for different kinds of knowledge reflected in different user goals. We developed a taxonomy to distinguishing when users are best served by these two approaches.
Accelerating Python Applications with Dask and ProxyStore
Venue: arXiv cs.DC 2024
Year: 2024

Applications are increasingly written as dynamic workflows underpinned by an execution framework that manages asynchronous computations across distributed hardware. However, execution frameworks typically offer one-size-fits-all solutions for data flow management, which can restrict performance and scalability. ProxyStore, a middleware layer that optimizes data flow via an advanced pass-by-reference paradigm, has shown to be an effective mechanism for addressing these limitations. Here, we investigate integrating ProxyStore with Dask Distributed, one of the most popular libraries for distributed computing in Python, with the goal of supporting scalable and portable scientific workflows. Dask provides an easy-to-use and flexible framework, but is less optimized for scaling certain data-intensive workflows. We investigate these limitations and detail the technical contributions necessary to develop a robust solution for distributed applications and demonstrate improved performance on synthetic benchmarks and real applications.
Bridging Large Language Models and Graph Structure Learning Models for Robust Representation Learning
Venue: arXiv cs.LG 2024
Year: 2024

Graph representation learning, involving both node features and graph structures, is crucial for real-world applications but often encounters pervasive noise. State-of-the-art methods typically address noise by focusing separately on node features with large language models (LLMs) and on graph structures with graph structure learning models (GSLMs). In this paper, we introduce LangGSL, a robust framework that integrates the complementary strengths of pre-trained language models and GSLMs to jointly enhance both node feature and graph structure learning. In LangGSL, we first leverage LLMs to filter noise in the raw data and extract valuable cleaned information as features, enhancing the synergy of downstream models. During the mutual learning phase in LangGSL, the core idea is to leverage the relatively small language model (LM) to process local attributes and generate reliable pseudo-labels and informative node embeddings, which are then integrated into the GSLM's prediction phase. This approach enriches the global context and enhances overall performance. Meanwhile, GSLM refines the evolving graph structure constructed from the LM's output, offering updated labels back to the LM as additional guidance, thus facilitating a more effective mutual learning process. The LM and GSLM work synergistically, complementing each other's strengths and offsetting weaknesses within a variational information-maximizing framework, resulting in enhanced node features and a more robust graph structure. Extensive experiments on diverse graph datasets of varying scales and across different task scenarios demonstrate the scalability and effectiveness of the proposed approach.
Expected Sliced Transport Plans
Venue: arXiv cs.LG 2024
Year: 2024

The optimal transport (OT) problem has gained significant traction in modern machine learning for its ability to: (1) provide versatile metrics, such as Wasserstein distances and their variants, and (2) determine optimal couplings between probability measures. To reduce the computational complexity of OT solvers, methods like entropic regularization and sliced optimal transport have been proposed. The sliced OT framework improves efficiency by comparing one-dimensional projections (slices) of high-dimensional distributions. However, despite their computational efficiency, sliced-Wasserstein approaches lack a transportation plan between the input measures, limiting their use in scenarios requiring explicit coupling. In this paper, we address two key questions: Can a transportation plan be constructed between two probability measures using the sliced transport framework? If so, can this plan be used to define a metric between the measures? We propose a "lifting" operation to extend one-dimensional optimal transport plans back to the original space of the measures. By computing the expectation of these lifted plans, we derive a new transportation plan, termed expected sliced transport (EST) plans. We prove that using the EST plan to weight the sum of the individual Euclidean costs for moving from one point to another results in a valid metric between the input discrete probability measures. We demonstrate the connection between our approach and the recently proposed min-SWGG, along with illustrative numerical examples that support our theoretical findings.
DAQ: Density-Aware Post-Training Weight-Only Quantization For LLMs
Venue: arXiv cs.LG 2024
Year: 2024

Large language models (LLMs) excel in various tasks but face deployment challenges due to hardware constraints. We propose density-aware post-training weight-only quantization (DAQ), which has two stages: 1) density-centric alignment, which identifies the center of high-density weights and centers the dynamic range on this point to align high-density weight regions with floating-point high-precision regions; 2) learnable dynamic range adjustment, which adjusts the dynamic range by optimizing quantization parameters (i.e., scale and zero-point) based on the impact of weights on the model output. Experiments on LLaMA and LLaMA-2 show that DAQ consistently outperforms the best baseline method, reducing perplexity loss by an average of 22.8% on LLaMA and 19.6% on LLaMA-2. Our code is available at https://github.com/LuoYingSong/DAQ.
LPUF-AuthNet: A Lightweight PUF-Based IoT Authentication via Tandem Neural Networks and Split Learning
Venue: arXiv cs.CR 2024
Year: 2024

By 2025, the internet of things (IoT) is projected to connect over 75 billion devices globally, fundamentally altering how we interact with our environments in both urban and rural settings. However, IoT device security remains challenging, particularly in the authentication process. Traditional cryptographic methods often struggle with the constraints of IoT devices, such as limited computational power and storage. This paper considers physical unclonable functions (PUFs) as robust security solutions, utilizing their inherent physical uniqueness to authenticate devices securely. However, traditional PUF systems are vulnerable to machine learning (ML) attacks and burdened by large datasets. Our proposed solution introduces a lightweight PUF mechanism, called LPUF-AuthNet, combining tandem neural networks (TNN) with a split learning (SL) paradigm. The proposed approach provides scalability, supports mutual authentication, and enhances security by resisting various types of attacks, paving the way for secure integration into future 6G technologies.
Test-time adaptation for image compression with distribution regularization
Venue: arXiv cs.CV 2024
Year: 2024

Current test- or compression-time adaptation image compression (TTA-IC) approaches, which leverage both latent and decoder refinements as a two-step adaptation scheme, have potentially enhanced the rate-distortion (R-D) performance of learned image compression models on cross-domain compression tasks, \textit{e.g.,} from natural to screen content images. However, compared with the emergence of various decoder refinement variants, the latent refinement, as an inseparable ingredient, is barely tailored to cross-domain scenarios. To this end, we aim to develop an advanced latent refinement method by extending the effective hybrid latent refinement (HLR) method, which is designed for \textit{in-domain} inference improvement but shows noticeable degradation of the rate cost in \textit{cross-domain} tasks. Specifically, we first provide theoretical analyses, in a cue of marginalization approximation from in- to cross-domain scenarios, to uncover that the vanilla HLR suffers from an underlying mismatch between refined Gaussian conditional and hyperprior distributions, leading to deteriorated joint probability approximation of marginal distribution with increased rate consumption. To remedy this issue, we introduce a simple Bayesian approximation-endowed \textit{distribution regularization} to encourage learning a better joint probability approximation in a plug-and-play manner. Extensive experiments on six in- and cross-domain datasets demonstrate that our proposed method not only improves the R-D performance compared with other latent refinement counterparts, but also can be flexibly integrated into existing TTA-IC methods with incremental benefits.
Implementation of EMR System in Indonesian Health Facilities: Benefits and Constraints
Venue: arXiv cs.CY 2024
Year: 2024

This paper delves into the widespread implementation of Electronic Medical Records (EMR) within healthcare facilities across Indonesia. It examines the driving forces behind EMR adoption, particularly the role of government regulations, and addresses the challenges encountered by clinic owners and healthcare providers in transitioning to these digital systems. Furthermore, this paper highlights the significant benefits and transformative advantages of EMR systems, such as enhanced decision-making through real-time data access (around 15-20 minutes time saved for patient waiting time and approximately saved 20-25 minutes for all service duration), reduction in healthcare costs over time due to improved resource management, and increased patient satisfaction by providing faster and more personalized care. EMR systems also ensure higher levels of data security and privacy, adhering to national healthcare standards, while supporting continuous monitoring and updates that enhance system resilience and functionality. The findings are substantiated through case studies, such as case study at LAPAS II Purwokerto Clinic and case study at PMI Purbalingga Clinic and user testimonials from clinics that have successfully implemented EMR solutions in compliance with the standards established by the Ministry of Communication and Informatics (Kominfo) and the Ministry of Health (Kemenkes).
Discovering Leitmotifs in Multidimensional Time Series
Venue: arXiv cs.LG 2024
Year: 2024

A leitmotif is a recurring theme in literature, movies or music that carries symbolic significance for the piece it is contained in. When this piece can be represented as a multi-dimensional time series (MDTS), such as acoustic or visual observations, finding a leitmotif is equivalent to the pattern discovery problem, which is an unsupervised and complex problem in time series analytics. Compared to the univariate case, it carries additional complexity because patterns typically do not occur in all dimensions but only in a few - which are, however, unknown and must be detected by the method itself. In this paper, we present the novel, efficient and highly effective leitmotif discovery algorithm LAMA for MDTS. LAMA rests on two core principals: (a) a leitmotif manifests solely given a yet unknown number of sub-dimensions - neither too few, nor too many, and (b) the set of sub-dimensions are not independent from the best pattern found therein, necessitating both problems to be approached in a joint manner. In contrast to most previous methods, LAMA tackles both problems jointly - instead of independently selecting dimensions (or leitmotifs) and finding the best leitmotifs (or dimensions). Our experimental evaluation on a novel ground-truth annotated benchmark of 14 distinct real-life data sets shows that LAMA, when compared to four state-of-the-art baselines, shows superior performance in detecting meaningful patterns without increased computational complexity.
LLM-based Cognitive Models of Students with Misconceptions
Venue: arXiv cs.HC 2024
Year: 2024

Accurately modeling student cognition is crucial for developing effective AI-driven educational technologies. A key challenge is creating realistic student models that satisfy two essential properties: (1) accurately replicating specific misconceptions, and (2) correctly solving problems where these misconceptions are not applicable. This dual requirement reflects the complex nature of student understanding, where misconceptions coexist with correct knowledge. This paper investigates whether Large Language Models (LLMs) can be instruction-tuned to meet this dual requirement and effectively simulate student thinking in algebra. We introduce MalAlgoPy, a novel Python library that generates datasets reflecting authentic student solution patterns through a graph-based representation of algebraic problem-solving. Utilizing MalAlgoPy, we define and examine Cognitive Student Models (CSMs) - LLMs instruction tuned to faithfully emulate realistic student behavior. Our findings reveal that LLMs trained on misconception examples can efficiently learn to replicate errors. However, the training diminishes the model's ability to solve problems correctly, particularly for problem types where the misconceptions are not applicable, thus failing to satisfy second property of CSMs. We demonstrate that by carefully calibrating the ratio of correct to misconception examples in the training data - sometimes as low as 0.25 - it is possible to develop CSMs that satisfy both properties. Our insights enhance our understanding of AI-based student models and pave the way for effective adaptive learning systems.
Correction to Local Information Privacy and Its Applications to Data Aggregation
Venue: arXiv cs.CR 2024
Year: 2024

In our previous works, we defined Local Information Privacy (LIP) as a context-aware privacy notion and presented the corresponding privacy-preserving mechanism. Then we claim that the mechanism satisfies epsilon-LIP for any epsilon>0 for arbitrary Px. However, this claim is not completely correct. In this document, we provide a correction to the valid range of privacy parameters of our previously proposed LIP mechanism. Further, we propose efficient algorithms to expand the range of valid privacy parameters. Finally, we discuss the impact of updated results on our original paper's experiments, the rationale of the proposed correction and corrected results.
Human-Inspired Long-Term Indoor Localization in Human-Oriented Environment
Venue: arXiv cs.RO 2024
Year: 2024

Lifelong localization is crucial for enabling the autonomy of service robots. In this paper, we present an overview of our past research on long-term localization and mapping, exploiting geometric priors such as floor plans and integrating textual and semantic information. Our approach was validated on challenging sequences spanning over many months, and we released open source implementations.
GAN Based Top-Down View Synthesis in Reinforcement Learning Environments
Venue: arXiv cs.CV 2024
Year: 2024

Human actions are based on the mental perception of the environment. Even when all the aspects of an environment are not visible, humans have an internal mental model that can generalize the partially visible scenes to fully constructed and connected views. This internal mental model uses learned abstract representations of spatial and temporal aspects of the environments encountered in the past. Artificial agents in reinforcement learning environments also benefit by learning a representation of the environment from experience. It provides the agent with viewpoints that are not directly visible to it, helping it make better policy decisions. It can also be used to predict the future state of the environment. This project explores learning the top-down view of an RL environment based on the artificial agent's first-person view observations with a generative adversarial network(GAN). The top-down view is useful as it provides a complete overview of the environment by building a map of the entire environment. It provides information about the objects' dimensions and shapes along with their relative positions with one another. Initially, when only a partial observation of the environment is visible to the agent, only a partial top-down view is generated. As the agent explores the environment through a set of actions, the generated top-down view becomes complete. This generated top-down view can assist the agent in deducing better policy decisions. The focus of the project is to learn the top-down view of an RL environment. It doesn't deal with any Reinforcement Learning task.
Beyond Coarse-Grained Matching in Video-Text Retrieval
Venue: arXiv cs.CV 2024
Year: 2024

Video-text retrieval has seen significant advancements, yet the ability of models to discern subtle differences in captions still requires verification. In this paper, we introduce a new approach for fine-grained evaluation. Our approach can be applied to existing datasets by automatically generating hard negative test captions with subtle single-word variations across nouns, verbs, adjectives, adverbs, and prepositions. We perform comprehensive experiments using four state-of-the-art models across two standard benchmarks (MSR-VTT and VATEX) and two specially curated datasets enriched with detailed descriptions (VLN-UVO and VLN-OOPS), resulting in a number of novel insights: 1) our analyses show that the current evaluation benchmarks fall short in detecting a model's ability to perceive subtle single-word differences, 2) our fine-grained evaluation highlights the difficulty models face in distinguishing such subtle variations. To enhance fine-grained understanding, we propose a new baseline that can be easily combined with current methods. Experiments on our fine-grained evaluations demonstrate that this approach enhances a model's ability to understand fine-grained differences.
AdaCropFollow: Self-Supervised Online Adaptation for Visual Under-Canopy Navigation
Venue: arXiv cs.RO 2024
Year: 2024

Under-canopy agricultural robots can enable various applications like precise monitoring, spraying, weeding, and plant manipulation tasks throughout the growing season. Autonomous navigation under the canopy is challenging due to the degradation in accuracy of RTK-GPS and the large variability in the visual appearance of the scene over time. In prior work, we developed a supervised learning-based perception system with semantic keypoint representation and deployed this in various field conditions. A large number of failures of this system can be attributed to the inability of the perception model to adapt to the domain shift encountered during deployment. In this paper, we propose a self-supervised online adaptation method for adapting the semantic keypoint representation using a visual foundational model, geometric prior, and pseudo labeling. Our preliminary experiments show that with minimal data and fine-tuning of parameters, the keypoint prediction model trained with labels on the source domain can be adapted in a self-supervised manner to various challenging target domains onboard the robot computer using our method. This can enable fully autonomous row-following capability in under-canopy robots across fields and crops without requiring human intervention.